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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) Bulletin of Electrical Engineering and Informatics JOIV : International Journal on Informatics Visualization
Nasution, Tigor Hamonangan
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Bike Fitting System Based on Digital Image Processing on Road Bike Nasution, Tigor Hamonangan; Sitohang, Andreas; Seniman, Seniman; Soeharwinto, Soeharwinto
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2796

Abstract

This research aims to develop a bike fitting system based on digital image processing for road bikes. The method used in this study involves using the OpenCV and MediaPipe libraries in the Python programming language to detect the rider's body pose from a video stream captured using a webcam. The body pose data is then used to calculate important angles such as elbow, hip, knee, and ankle range related to the correct riding position for road bikes. In this research, a comparison is made between the body angles obtained and the angle range considered ideal for bike fitting on road bikes. If the body angles fall within the desired range, the system will label it as "Fit”; if the body angles are outside the selected range, the system will label it as "Not Fit." The results of this study indicate that the bike fitting system based on digital image processing using a webcam can provide helpful visual feedback in improving the rider's body position for road bikes. By observing the body angles produced and seeing the "Fit" or "Not Fit" label, cyclists can adjust their position to match the ideal position in bike fitting. The system test results show a low error rate, with elbow angle having an average error of 0.81%, hip angle of 1.37%, knee angle of 0.83%, and ankle range of 1.76%. Thus, this research contributes significantly to supporting cyclists in achieving a position appropriate to their inseam height.
The implementation of the K-nearest neighbor algorithm to detect the KRSRI robot obstacles Hamonangan Nasution, Tigor; Muhammad Prihandoyo, Arza; Seniman, Seniman
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8225

Abstract

The Indonesian SAR robot contest (KRSRI) is a development of the fire extinguisher robot contest (KRPAI); initially, the robot at KRPAI only put out fires. Still, at KRSRI, the robot was asked to prioritize the SAR function. The robot had to overcome obstacles in this contest to complete it. Based on this, an obstacle detection system for the robot was designed using machine learning with the K-nearest neighbor algorithm and gray level co-occurrence matrix feature extraction. Later, the robot is expected to be able to carry out accurate obstacle detection to prioritize efficiency so that no more time is consumed due to the robot incorrectly detecting an obstacle. The results of the tests that have been carried out show that the detection accuracy based on the test dataset is 80% for rising barriers, 100% for debris obstacles, and 90% for step obstacles, and an error value of 20% for increasing obstacles is obtained, 0% for debris obstacles, and 10% for stair obstacles.
Architectural design of an internet of things-based framework for road bike speed optimization Tigor Hamonangan Nasution; Opim Salim Sitompul; Fahmi Fahmi; Muhammad Anggia Muchtar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2125-2140

Abstract

This research aims to develop an internet of things (IoT) system framework to predict cyclists’ optimal speed in road cycling using multisensor data and machine learning. The primary issue raised is the lack of an intelligent system capable of integrating physiological, performance, and environmental data in real-time speeds for cyclists. The designed framework consists of four functional layers: data acquisition layer; data processing and feature layer; predictive modeling layer; and recommendations and output layer. Modeling is carried out using gradient boosting regression (GBR), performed end-to-end with validation on real cyclist activity data. The test results demonstrate that the system can provide precise optimal speed estimates and offer pacing zone recommendations that positively impact athlete performance strategies. This research contributes novelty in the form of an adaptive multivariate prediction approach and a modular IoT architecture design that can be implemented on cloud and edge platforms.
Mobile device application design for ThingSpeak interface using flutter Moehammad Sauqy Ihza Zuliandra; Tigor Hamonangan Nasution; Ainul Hizriadi
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp850-860

Abstract

The rapid development of internet of things (IoT) is prompting many people to design applications, particularly for monitoring applications based on mobile apps. This includes research designs to monitor electrical parameters from PV and the development of health monitoring applications. Previous research required a separate application to scan each IoT device. In this research, a mobile app-based IoT monitoring system was built using flutter. With this, people no longer need to design separate mobile apps for various IoT devices. This application utilizes the flutter framework, while the cloud component uses ThingSpeak. These research results show that data from multiple IoT devices can be transferred to the user’s mobile app. This application enables the monitoring of various IoT devices through a single mobile app, thereby enhancing the efficiency of IoT device design and management.
Co-Authors Ainul Hizriadi Fahmi Fahmi Moehammad Sauqy Ihza Zuliandra Muhammad Anggia Muchtar Muhammad Prihandoyo, Arza Opim Salim Sitompul Seniman, Seniman Sitohang, Andreas Soeharwinto Soeharwinto, Soeharwinto